code: def cholesky_to_lower_triangular(R):
      """Convert Cholesky decomposition elements to the diffusion tensor elements
  
      Parameters
      ----------
      R : array (6,)
          Array containing the six Cholesky's decomposition elements
          (R0, R1, R2, R3, R4, R5) [1]_.
  
      Returns
      -------
      tensor_elements : array (6,)
          Array containing the six elements of diffusion tensor's lower
          triangular.
  
      References
      ----------
      .. [1] Koay, C.G., Carew, J.D., Alexander, A.L., Basser, P.J.,
             Meyerand, M.E., 2006. Investigation of anomalous estimates of
             tensor-derived quantities in diffusion tensor imaging. Magnetic
             Resonance in Medicine, 55(4), 930-936. doi:10.1002/mrm.20832
      """
      Dxx = R[0] ** 2
      Dxy = R[0] * R[3]
      Dyy = R[1] ** 2 + R[3] ** 2
      Dxz = R[0] * R[5]
      Dyz = R[1] * R[4] + R[3] * R[5]
      Dzz = R[2] ** 2 + R[4] ** 2 + R[5] ** 2
      return np.array([Dxx, Dxy, Dyy, Dxz, Dyz, Dzz])
  
imports: import warnings
  import numpy
  import scipy.optimize
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import TensorFit, design_matrix, decompose_tensor, _decompose_tensor_nan, from_lower_triangular, lower_triangular
  from dipy.reconst.dki import _positive_evals
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.core.ndindex import ndindex
  from dipy.core.gradients import check_multi_b
  from dipy.reconst.multi_voxel import multi_voxel_fit
label_desc: Use image node_id 9 for calling a global function with example usage: cholesky_to_lower_triangular(R) and returns: np